Inbound parcel volume forecasting problem (IPVFP) plays an important role in the logistics system as it can facilitate various downstream applications. Despite the fact that a number of time series forecasting techniques have been developed, existing approaches fail to explicitly consider intrinsic characteristics of the logistics system, e.g., parcel transport patterns, operation patterns, and their spatial-temporal dependencies. To this end, we propose a novel hybrid inbound parcel volume forecasting model to analyze the logistic spatial-temporal graph that is constructed based on logistics data and the physical location of logistics stations. The graph includes engineered features such as the transition matrix and modified Dynamic Time Warping (DTW) distance matrix, which accurately depicts the parcel transfer patterns within the system. In addition, it incorporates a dedicated attention mechanism that introduces a novel bit-embedding representation method for integer tokens, enabling to capture of dynamic correlations among different timestamps. Finally, a collaborative module comprising dilated convolution layers and Gated Recurrent Units (GRU) is integrated to capture long-term dependencies. Extensive experiments on real-world data evaluate the effectiveness of the proposed graph and model, demonstrating its superiority over 16 other advanced baseline models. We release our code and data at https://github.com/YelsAlyssa/IPVFP.

MOTIVATION: Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. RESULTS: Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single- and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive with other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/QDistance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.